Global ETD Search

1	An Analysis of Changes In Work Trip Travel Behaviour Lo, Pui-Chin 12 1900 (has links) <p> This work trip study is part of the studies on King Street closure. The objectives are to examine the effect of changed traffic conditions on change in travel behaviour, and to identify variables for choice modelling. Some behavioural changes are observed, but none is related to the increased road congestion. The household survey data shows that people did not perceive a difference in travel times before and during closure. Thus the reliability of reported times on modelling is suspected. However, modelling on time of day in a multinomial legit framework using measured travel data does not help to explain the behavioural changes with either travel time or a congestion factor. It is concluded that the changes observed in this study represent random occurrences and the change in congestion is too moderate to effect behavioural changes. </p> / Thesis / Master of Arts (MA) work trip travel travel behaviour traffic conditions road congestion
2	The Joint Modelling of Trip Timing and Mode Choice Day, Nicholas 24 February 2009 (has links) This thesis jointly models the 24 hour work trip timing and mode choice decisions of commuters in the Greater Toronto Area. A discrete-continuous specification, with a multinomial logit model for mode choice and an accelerated time hazard model for trip timing, is used to allow for unrestricted correlation between these two fundamental decisions. Statistically significant correlations are found between mode choice and trip timing for work journeys with expected differences between modes. Furthermore, the joint models have a wide range of policy sensitive statistically significant parameters of intuitive sign and magnitude, revealing expected differences between workers of different occupation groups. Furthermore, the estimated models have a high degree of fit to observed cumulative departure and arrival time distribution functions and to observed mode choices. Finally, sensitivity tests have demonstrated that the model is capable of capturing peak spreading in response to increasing auto congestion. travel behaviour work trip timing mode choice hazard model logit model joint model peak spreading time of day choice 0709
3	The Joint Modelling of Trip Timing and Mode Choice Day, Nicholas 24 February 2009 (has links) This thesis jointly models the 24 hour work trip timing and mode choice decisions of commuters in the Greater Toronto Area. A discrete-continuous specification, with a multinomial logit model for mode choice and an accelerated time hazard model for trip timing, is used to allow for unrestricted correlation between these two fundamental decisions. Statistically significant correlations are found between mode choice and trip timing for work journeys with expected differences between modes. Furthermore, the joint models have a wide range of policy sensitive statistically significant parameters of intuitive sign and magnitude, revealing expected differences between workers of different occupation groups. Furthermore, the estimated models have a high degree of fit to observed cumulative departure and arrival time distribution functions and to observed mode choices. Finally, sensitivity tests have demonstrated that the model is capable of capturing peak spreading in response to increasing auto congestion. travel behaviour work trip timing mode choice hazard model logit model joint model peak spreading time of day choice 0709
4	GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling / GIS-based Episode Reconstruction Using GPS Data Dalumpines, Ron 26 September 2014 (has links) Most transportation problems arise from individual travel decisions. In response, transportation researchers had been studying individual travel behavior – a growing trend that requires activity data at individual level. Global positioning systems (GPS) and geographical information systems (GIS) have been used to capture and process individual activity data, from determining activity locations to mapping routes to these locations. Potential applications of GPS data seem limitless but our tools and methods to make these data usable lags behind. In response to this need, this dissertation presents a GIS-based toolkit to automatically extract activity episodes from GPS data and derive information related to these episodes from additional data (e.g., road network, land use). The major emphasis of this dissertation is the development of a toolkit for extracting information associated with movements of individuals from GPS data. To be effective, the toolkit has been developed around three design principles: transferability, modularity, and scalability. Two substantive chapters focus on selected components of the toolkit (map-matching, mode detection); another for the entire toolkit. Final substantive chapter demonstrates the toolkit’s potential by comparing route choice models of work and shop trips using inputs generated by the toolkit. There are several tools and methods that capitalize on GPS data, developed within different problem domains. This dissertation contributes to that repository of tools and methods by presenting a suite of tools that can extract all possible information that can be derived from GPS data. Unlike existing tools cited in the transportation literature, the toolkit has been designed to be complete (covers preprocessing up to extracting route attributes), and can work with GPS data alone or in combination with additional data. Moreover, this dissertation contributes to our understanding of route choice decisions for work and shop trips by looking into the combined effects of route attributes and individual characteristics. / Dissertation / Doctor of Philosophy (PhD) GPS time use diary episode extraction multinomial logit travel behavior mode detection episode reconstruction GIS map-matching route choice path size logit potential path area scale estimation Python ArcGIS activity analysis trip reconstruction smartphone global positioning system geographic information system toolkit work trip shop trip potential activity location land use activity episode travel episode stop episode transferability scalability modularity scripting big data travel survey respondent burden preprocessing multipath error tracking purpose detection segmentation data filter data smoothing fuzzy logic neural network decision tree rule-based algorithm trajectory point road network network dataset gateway shortest path horizontal dilution of precision HDOP commonality factor mode transfer point Halifax Nova Scotia Space-Time Activity Research variance inflation factor shapefile comma-separated values traveling salesman problem data mining branch-and-bound algorithm pedestrian network alternative route observed route route efficiency route attributes distance time heading bearing duration acceleration latitude longitude coordinate overlay analysis intersect shopping module data logger walk likelihood ratio test classification table path generation kappa statistic degrees data collection transportation research automate framework ArcToolbox spatio-temporal navigation positioning trace path spatial data topology horizontal accuracy SPSS Stata spatial resolution temporal resolution household survey trip reporting proximity analysis DMTI Desktop Mapping Technologies Inc. road intersection route overlap left turn right turn location analysis time geography spatial statistics buffer analysis cycling bus transit public transportation GEOIDE AGILE data need raw data urban canyon endpoint data cleaning elevation satellite outliers automatic processing nearest node classifier classification method short trip multi-point

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